Signals are often processed to identify the nature of the received signal and/or to extract certain events from received signals. For example, with respect to electronic intelligence (ELINT) applications, whether or not a repetitive signal is being received and the nature of this repetitive signal must be determined because the source is typically an unknown signal source that is being detected by the ELINT system. Extraction of signals from raw signal data can require that the events and modulation techniques be detected within the raw signal data. Examples of modulation techniques that may be present in received signals include repetitive synchronized events such as phase transitions in a phase-shift key (PSK) signal and frequency transitions in a frequency-shift key (FSK) signal.
The detection of unknown signals and signal patterns are particularly difficult in low signal-to-noise ratio (SNR) environments are encountered. For example, it becomes difficult to distinguish PSK signals from FSK signals (and vice versa) when noisy signal data is being received. One prior solution has attempted to overcome these low SNR problems by recovering data bits using a match filter or correlation approach. The purpose of this approach is to derive, in relation to the SNR, the error probability in bit recovery. This approach, however, assumes that the system has knowledge of the start time, minimum interval, and the PSK carrier frequency and phases for PSK signals or FSK frequencies for FSK signals. As such, significant information concerning the signals being received must be known prior to reception.